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scoring_micro_macro_weighted.py
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scoring_micro_macro_weighted.py
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import numpy as np
f=open('scoring.dat')
ypred=[]
yref=[]
for line in f:
l=line.split()
ypred.append(l[1])
yref.append(l[2])
#print(ypred)
#print(yref)
def accuracy(a,b):
N=len(a)
count=0
for i in range(N):
if a[i]==b[i]:
count=count+1
return count/float(N)
def weighted_accuracy(a,b):
N=len(a)
label=np.unique(a)
weight=np.zeros(len(label),dtype=float)
for j in range(len(label)):
for i in range(N):
if a[i]==label[j]:
weight[j]+=1
for j in range(len(label)):
weight[j]=weight[j]/N
total=[]
for j in range(len(label)):
count=0.0
classwisecount=0
for i in range(N):
if a[i]==label[j]:
classwisecount+=1
if a[i]==b[i]:
count=count+1
#print(weight[j],count/classwisecount)
total.append(count/float(classwisecount))
return [np.mean(total), total]
'''
Reference
Prediction A B C D E
A 35 0 0 0 2
B 0 9 5 0 2
C 0 0 10 2 0
D 5 1 0 23 0
E 5 0 0 0 1
'''
#none
mat=[[ 44 , 29 , 33 , 29, 27 , 28 , 26, 33, 2],
[ 52 ,118, 55, 67, 75, 89, 66, 41, 2],
[ 21, 28, 21, 15, 15, 25, 21, 14, 0],
[144, 177, 140, 185, 156, 174, 114, 132, 9],
[ 21, 38, 24, 24, 30, 33, 23, 15, 3],
[ 17, 67, 50, 31, 31, 53, 23, 26, 0],
[193, 292, 184, 213, 223, 250, 199, 164, 9],
[719, 864, 668, 808, 767, 860, 644, 621, 20],
[ 0, 0, 2, 1, 0, 2, 0, 0, 4]]
'''
#true
mat=[[0.1752988 , 0.11553785 ,0.1314741 , 0.11553785 ,0.10756972 ,0.11155378,
0.10358566 ,0.1314741 , 0.00796813],
[0.0920354 , 0.20884956 ,0.09734513, 0.11858407, 0.13274336, 0.15752212,
0.11681416, 0.07256637, 0.00353982],
[0.13125 , 0.175, 0.13125, 0.09375, 0.09375, 0.15625,
0.13125 , 0.0875 , 0. ], [0.11697807 ,0.14378554 ,0.11372868 ,0.15028432 ,0.12672624, 0.1413485,
0.09260764, 0.10722989, 0.00731113],
[0.09952607, 0.18009479, 0.11374408, 0.11374408, 0.14218009, 0.1563981,
0.10900474, 0.07109005, 0.01421801],
[0.05704698, 0.22483221, 0.16778523, 0.10402685, 0.10402685, 0.17785235,
0.07718121, 0.08724832, 0. ],
[0.11175449, 0.16907933, 0.10654314, 0.12333526, 0.12912565, 0.1447597,
0.11522872, 0.09496236, 0.00521135],
[0.12041534, 0.14469938, 0.11187406, 0.13532072, 0.1284542, 0.14402948,
0.10785463, 0.10400268, 0.00334952], [0. , 0. , 0.22222222 ,0.11111111 ,0. , 0.22222222,
0. , 0. , 0.44444444]]
#pred
mat=[[0.03633361 ,0.01797892, 0.02803738, 0.02112163, 0.02039275, 0.01849406,
0.02329749, 0.03154876, 0.04081633],
[0.04293972 ,0.07315561, 0.04672897 ,0.04879825, 0.05664653 ,0.05878468,
0.05913978, 0.03919694, 0.04081633],
[0.01734104, 0.01735896, 0.01784197, 0.01092498, 0.01132931, 0.01651255,
0.0188172, 0.01338432, 0. ],
[0.11890999, 0.10973342, 0.11894647, 0.13474144, 0.11782477, 0.11492734,
0.10215054, 0.12619503, 0.18367347],
[0.01734104 ,0.02355859, 0.02039082, 0.01747997, 0.02265861, 0.02179657,
0.02060932, 0.01434034, 0.06122449],
[0.01403799, 0.04153751, 0.04248088, 0.0225783, 0.0234139, 0.03500661,
0.02060932, 0.0248566, 0. ]
, [0.15937242, 0.18102914, 0.15632965 ,0.15513474 ,0.168429 , 0.1651255,
0.17831541, 0.15678776 ,0.18367347],
[0.59372419, 0.53564786, 0.5675446 , 0.58849235, 0.57930514, 0.5680317,
0.57706093, 0.59369025 ,0.40816327],
[0. , 0. , 0.00169924 ,0.00072833 ,0. , 0.001321,
0., 0. , 0.08163265]]
'''
def macro_avg(a,b):
N=len(a)
label=np.unique(a)
TP=[35,9,10,23,1]
FP=[2,7,2,6,5]
FN=[10,1,5,2,4]
TP=[]
FP=[]
FN=[]
for i in range(len(mat)):
#print(mat[i][1])
TP.append(mat[i][i])
rowsum=0
colsum=0
for j in range(len(mat)):
if j!=i:
colsum+=mat[i][j]
rowsum+=mat[j][i]
FN.append(colsum)
FP.append(rowsum)
#print(TP,'\n',FP,'\n',FN)
Pre=[]
Rec=[]
F1=[]
for k in range(len(TP)):
v1=TP[k]/float(TP[k]+FP[k])
v2=TP[k]/float(TP[k]+FN[k])
Pre.append(v1)
Rec.append(v2)
F1.append(2*v1*v2/(v1+v2))
print("Pre",Pre)
print("Rec",Rec)
Pre=sum(Pre)/len(TP)
Rec=sum(Rec)/len(TP)
F1= sum(F1)/len(TP)
return (Pre,Rec,F1)
def micro_avg(a,b):
N=len(a)
label=np.unique(a)
TP=[35,9,10,23,1]
FP=[2,7,2,6,5]
FN=[0,0,0,0,0]
#FN=[10,1,5,2,4]
TP=[]
FP=[]
FN=[]
for i in range(len(mat)):
#print(mat[i][1])
TP.append(mat[i][i])
rowsum=0
colsum=0
for j in range(len(mat)):
if j!=i:
colsum+=mat[i][j]
rowsum+=mat[j][i]
FN.append(colsum)
FP.append(rowsum)
tp=sum(TP)
fp=sum(FP)
fn=sum(FN)
Pre= tp / (tp+fp)
Rec= tp / (tp+fn)
F1= 2*Pre*Rec/(Pre+Rec)
return (Pre,Rec,F1)
def weight_avg(a,b):
N=len(a)
weight=[0.024035117177193026, 0.05423493007747847, 0.015327784307611121, 0.11811269159730385, 0.020293122886133032, 0.028544700040778144, 0.1657271702367531, 0.5728609465326585, 0.0008635371440907674]
TP=[]
FP=[]
FN=[]
for i in range(len(mat)):
#print(mat[i][1])
TP.append(mat[i][i])
rowsum=0
colsum=0
for j in range(len(mat)):
if j!=i:
colsum+=mat[i][j]
rowsum+=mat[j][i]
FN.append(colsum)
FP.append(rowsum)
Pre=[]
Rec=[]
F1=[]
for k in range(len(TP)):
v1=TP[k]/float(TP[k]+FP[k])
v2=TP[k]/float(TP[k]+FN[k])
Pre.append(v1)
Rec.append(v2)
F1.append(2*v1*v2/(v1+v2))
for i in range(len(TP)):
Pre[i]=Pre[i]*weight[i]
Rec[i]=Rec[i]*weight[i]
F1[i]=F1[i]*weight[i]
Pre=sum(Pre)/sum(weight)
Rec=sum(Rec)/sum(weight)
F1= sum(F1)/sum(weight)
return (Pre,Rec,F1)
print("accuracy",accuracy(yref,ypred))
print("weighted accuracy",weighted_accuracy(yref,ypred))
print("macro (P, R, F1) avg",macro_avg(yref,ypred))
print("micro avg",micro_avg(yref,ypred))
print("weight (P, R, F1) avg",weight_avg(yref,ypred))